Machine control system providing actionable management information and insight using agricultural telematics

ABSTRACT

A machine control system includes an agricultural work machine having an ECU coupled via a system bus to control engine functions, a GPS receiver, data collector, and specialized guidance system including a stored program. The data collector captures agricultural geospatial data including location data for the work machine and data from the ECU, and executes the stored program to: (a) capture geometries of the farm; (b) capture agricultural geospatial data; (c) automatically classify the agricultural geospatial data using the geometries of the farm, into activity/event categories including operational, travel, and ancillary events; (d) aggregate the classified data to create geospatial data events; (e) match the geospatial data events to a model to generate matched events; (f) use the matched events to generate actionable information for the working machine in real time or near real-time; and (g) send operational directives to the agricultural work machine based on the actionable information.

RELATED APPLICATION

This application claims the benefit and is a Continuation of U.S. patentapplication Ser. No. 16/043,229, entitled Machine Control SystemProviding Actionable Management Information and Insight UsingAgricultural Telematics, filed on Jul. 24, 2018, which claims thebenefit of U.S. Provisional Patent Application Ser. No. 62/536,026,entitled Actionable Management Information and Insight System forAgricultural Telematics (AMIISAT), filed on Jul. 24, 2017, and which isa Continuation-In-Part of U.S. patent application Ser. No. 15/490,791,(now U.S. Pat. No. 10,368,475) entitled Machine Guidance for OptimalWorking Direction of Travel, filed on Apr. 18, 2017, the contents ofwhich are incorporated herein by reference in their entirety for allpurposes.

BACKGROUND Technical Field

This invention relates to a machine control system for agriculturalequipment, and more specifically, to a system and method that usesreal-time, or near real-time, adaptive analysis to provide actionablemanagement information and operational directives to agriculturalmachines using agricultural geospatial data.

Background Information

Actionable information and insights to anyone associated withperformance metrics on an operating farm, including equipment operators,farm managers, and farm owners, is vital in order for the bestmanagement decisions to be made. Not only do operating farms havedecisions to make, but sectors such as the agricultural retail sector ofthe industry also have to manage decisions involving equipmentperformance metrics. A major source of difficulty in this management canbe attributed to properly managing the associated operating equipmentand the corresponding data that it can generate. Equipment logistics,usage, and cost, as well as operating metrics like machine data,agronomic data, and monitor/controller data are just a few examples ofparameters that may need to be evaluated in order to help with thosedecisions. The collection and analysis of this geospatial data can alsotake many man hours and be computationally expensive in current farmmanagement information systems.

The current collection and analysis process of said geospatial datausually includes first collecting the data from a multitude of differentsources, e.g., from a portable storage medium such as a USB flash driveor an external hard drive, cloud/web application data services, datastorage from databases, as well as directly from collection devices.These data sources have historically collected and stored the data fromthe incoming data streams so that further processing may be done at alater time. The data sources are also usually located in many differentplaces, both physically and in terms of connected networks, andtherefore are not directly part of a centralized system. They are also,very often, unstandardized in terms of the type of data that is stored.Stored data types for agricultural data may include field boundary data,agronomic data, machine, agronomic, or monitor/controller telematicsdata, as well as farm equipment information which may include, equipmentfinancial information, equipment configurations, and the variouspotential activities/uses of said equipment. These different data typesare often also stored in different data formats corresponding todifferent file types. The case often exists where even different dataformats and file types may occur for the same data type if the dataoriginates from different collection sources. Due to this, the incomingdata must be retrieved, standardized, and merged in order to providerelevance. In order to provide this sort of context, which also helps tomake the data actionable, the data must first be organized to determinewhich of the data is performing operational work. The non-operationalcategory is any data that is not considered to be performing work on agiven geospatial geometry of the farm. After the data has beenclassified, it must then be analyzed in order to create enoughsignificant context in order for actionable information and insights tobe generated. Once this context has been generated, further farm models,capacity, and financial business logic may be applied to help extend theinformation and insights provided.

The actual analysis process used to classify, assign, and aggregate thedata is often rigorous and manual by nature, but must occur in order toprovide the necessary context for accurate analysis. Processing thelarge amount of telematics data along with said equipmentconfigurations, and the various potential activities/uses of saidequipment for all geospatial events, over all geospatial geometries, forall potential scenarios becomes extremely expensive in terms ofcomputational power, time, and efficiency. This process, then, onlybecomes more complicated and expensive when data must be processed formultiple farms or agricultural retail operations.

Not only is the current process computationally expensive andinefficient, but the tools and skills required to the complete theanalysis are often numerous. Tools such as geographic informationsystems (GIS) software, data processing software, and data visualizationsoftware are required to complete the analysis from start to finish.With the increasing complexity in farm management situations and as theamount of data and potential scenarios increase, it can make these toolsvery time consuming to use. The skillset needed to operate thesedifferent software packages along with the skillset to physically movethe data from one to another is also one that not all equipmentoperators, farm managers, or farm owners possess. Due to this, it couldpotentially leave these operators, managers, and/or owners at acompetitive disadvantage when it comes to making decisions with thedata.

Therefore, a need has been shown for a system and process that addressesand improves upon the aforementioned issues.

SUMMARY OF THE INVENTION

The appended claims may serve as a summary of the invention. Thefeatures and advantages described herein are not all-inclusive andvarious embodiments may include some, none, or all of the enumeratedadvantages. Additionally, many additional features and advantages willbe apparent to one of ordinary skill in the art in view of the drawings,specification, and claims. Moreover, it should be noted that thelanguage used in the specification has been principally selected forreadability and instructional purposes, and not to limit the scope ofthe inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and notlimitation in the figures of the accompanying drawings, in which likereferences indicate similar elements and in which:

FIG. 1 illustrates a diagram of agricultural geospatial data collectionand transfer through the use of a cloud enabled network to a guidancesystem, which contains an embedded remote server-client architecture,coming from agricultural equipment with data collection capabilities, inaccordance with embodiments of the present invention.

FIG. 2 illustrates a simplified diagram of an equipment system datatransfer bus that resides on typical agricultural equipment to allow fordata collection, transfer, and control.

FIG. 3 illustrates a system of the collection and transfer ofagricultural geospatial data from a group of agricultural equipment aswell as the process and method of the generation of actionablemanagement information and insights.

FIG. 4 illustrates a diagram displaying a collection of geospatialgeometries.

FIG. 5 illustrates a diagram displaying agricultural equipmentperforming field operation events on multiple geospatial geometrieswhile transmitting agricultural geospatial data to a cloud enablednetwork.

FIG. 6 is a detailed block diagram of the server.

FIG. 7 is a detailed block diagram of the adaptive data analysisalgorithm which details the creation of the summarized agriculturalgeospatial data events.

FIG. 8 is an example of the summarized agricultural geospatial dataevents for a field boundary.

FIG. 9 is block diagram of the client.

FIG. 10 is a block diagram of the agricultural operations model.

FIG. 11 is an example of the planned chronological list of equipmentoperation events for a field boundary.

FIG. 12 is a detailed block diagram of the dynamicclassification/matching algorithm that details the generation ofactionable management information and insights.

FIG. 13 is an example of actionable management information generated byembodiments of the present invention in terms of the percentage of timespent at 0 mph for operation events.

FIG. 14 is an example of actionable management information generated byembodiments of the present invention in terms of a $/acre cost breakdownof operation events.

FIG. 15 shows an example of actionable management insights generated byembodiments of the present invention in terms of relating planting andharvesting speed to potential savings by increasing the averageoperation speed.

FIG. 16 is a block diagram of an exemplary computer usable in aspects ofembodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

It should be understood at the outset that, although exemplaryembodiments are illustrated in the figures and described below, theprinciples of the present disclosure may be implemented using any numberof techniques, whether currently known or not. The present disclosureshould in no way be limited to the exemplary implementations andtechniques illustrated in the drawings and described below.Additionally, unless otherwise specifically noted, articles depicted inthe drawings are not necessarily drawn to scale. In addition, well-knownstructures, circuits and techniques have not been shown in detail inorder not to obscure the understanding of this description. Thefollowing detailed description is, therefore, not to be taken in alimiting sense, and the scope of the present invention is defined by theappended claims and their equivalents.

General Overview

An aspect of the present invention was the realization by the instantinventors that without the association of the data to the propergeospatial geometry (field boundary or significant location) as well asto the geospatial event (operation), the use of the data is limited asto the information and insights that it is able to provide. It wasfurther recognized that in order to make these assignments, knowledge ofthe location of the geospatial geometries, the equipment and theirconfigurations, and temporal aggregation techniques must be known. Asmany farms contain a plurality of geospatial geometries, as well as manygeospatial events that occur at or on each of these geospatialgeometries, the classification, assignment, and aggregation processtends to be very complicated. That combined with the potential for anextremely large amount of incoming data, from different sources, canalso make efficient use of such data extremely difficult.

The instant inventors recognized that in agriculture, a majority of thetime equipment is driven from work location to work location, such asfrom field to field, to perform operational work. During this travelperiod, extra time, distance, and fuel, just to name a few, are beingaccrued which affects the logistics, the overall farming operation, andits associated costs. This is especially true in cases such as farmswith fields that are spread out over a large area, or pieces ofequipment like a self-propelled sprayer that may make many trips tofields throughout the growing season. Without analyzing travel data, andthe associated time and money that it attributes to the equipment andthe total farming operation, a complete picture of equipment usage andits effects cannot be seen. This incomplete picture of equipment usagecould potentially lead to uninformed management decisions. The sameholds true with any geospatial data resulting as ancillary to theoperational or travel data. The time and money accrued when theequipment is active in the barnyard, for example, must also be accountedfor in order to obtain a comprehensive look at all equipment activitiesand their costs.

The prior art known to the present inventors does not focus on theclassification of agricultural geospatial data in an adaptive manner. Asused herein, the term “adaptive” refers to the concept that thegeospatial data itself, using its own measured parameters, provides abasis for the classification outcome. The known prior art also fails todiscuss dynamically classifying/matching summarized data events to anagricultural operations model. The instant inventors further recognizedthat classifying summarized geospatial data events to an operationsmodel, e.g., matching actual operation events to planned operationevents, or assigning operation information to operation events, startsturning the data into something actionable. As operation type, andindividual operations, are categories within which farms make decisions,having information in these terms may prove to be a managementadvantage. This, coupled with dynamically re-classifying andre-evaluating the data, as any portion of the agricultural operationmodel changes, to provide substantially real-time or near real-timeresults, may provide an immediate advantage to the farming operation. Inmany applications, these aspects may improve the quality of theinformation and insights generated, the actionable decisions made, andultimately the overall performance and bottom line of the farmingoperation as a whole.

Therefore, the inventive embodiments discussed hereinbelow provide asystem and method that generates actionable management information,insights, and operational directives from telematics and agriculturalgeospatial data through a passive, automated, adaptive, and dynamicprocess.

More specifically, these embodiments provide for: the passive collectionand transfer of agricultural geospatial data, via telemetry, from activeagricultural equipment; the automatic and adaptive classification ofcollected geospatial data into aggregated operational, travel, andancillary data events; the summarization of said aggregated operational,travel, and ancillary data events; the methods of dynamicallyclassifying/matching the summarized data events to an agriculturaloperations model for the dynamic generation of actionable managementinformation and insights in real-time, in-season, and/or historically;and the transfer of this information to an agricultural machine in theform of operational directives.

As discussed in greater detail hereinbelow, agricultural geospatial datamay be collected from a collection device that is communicably coupledto an equipment system bus for the gathering of data that is beingcommunicated on the equipment's functional systems. This data may thenbe transmitted, via telemetry, to a cloud enabled network, and then to aserver for analysis in the system. The agricultural geospatial data mayalso be collected from sources that are not directly within thecentralized analysis system but have already stored raw data, such asgeospatial data databases, or external storage media such as a USB flashdrive or external hard drive. These data sources may also be accessed sothat both incoming data collected from collection devices and externalsources may be analyzed with the adaptive data analysis algorithm. Thealgorithm may adaptively classify the agricultural geospatial data intooperational, travel, or ancillary categories as the data is arrivinginto the system for effective processing and storage of the incomingdata, or after the data has been stored. The algorithm also assigns theclassified data to a geospatial geometry, such as a field boundary or asignificant location to the farming operation. The algorithm is thenable to aggregate and summarize the classified and assigned data inorder to create summarized agricultural geospatial data events for eachof the three classifications and for all known geospatial geometries.This adaptive classification analysis may be completed with only thehelp of the geospatially located geometries and the agriculturalgeospatial data itself, which contain the necessary information forclassification. Through a geospatial relation of the agriculturalgeospatial data and the geospatial geometries, as well as parametersfrom the agricultural geospatial data, classification to operational,travel, and ancillary activities may occur. In this way field boundariesand/or significant locations associated with the farm may be evaluated.A temporal analysis may then be completed within the algorithm toaggregate the classified agricultural geospatial data and summarize theresults in order for the generation of summarized geospatial dataevents.

The agricultural geospatial data events may also be transferred, via acommunication network, to a client for further processing in a dynamicclassification algorithm. This algorithm may use the help of anagricultural operations model, as well as capacity, financial, and/orbusiness logic to generate further information and insights on thecontextualized geospatial data. Management categories such as, businessfarm entities or clients, land ownership entities or farms, fields,operation event types, specific operation events, and/or specificequipment may be used with information and insights generated from thegeospatial data to help make actionable management decisions on thefarm.

In particular embodiments, the agricultural operations model alsoprovides a chronological list of operation events that have beenpre-planned and contain similar summarization characteristics, includingcost of operation parameter characteristics. These summarized operatingcharacteristics may then be used to classify the agricultural geospatialdata events into either an event that matches an event in the plannedlist of events, one that doesn't match any in the planned list ofevents, or one that is not found in the planned list of events. Theseinsights and information may then also be used by farm managers to helpthem make actionable management decisions on the farm.

Terminology

As used in the specification and in the appended claims, the singularforms “a”, “an”, and “the” include plural referents unless the contextclearly indicates otherwise. For example, reference to “an analyzer”includes a plurality of such analyzers. In another example, reference to“an analysis” includes a plurality of such analyses.

Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation. Allterms, including technical and scientific terms, as used herein, havethe same meaning as commonly understood by one of ordinary skill in theart to which this invention belongs unless a term has been otherwisedefined. It will be further understood that terms, such as those definedin commonly used dictionaries, should be interpreted as having a meaningas commonly understood by a person having ordinary skill in the art towhich this invention belongs. It will be further understood that terms,such as those defined in commonly used dictionaries, should beinterpreted as having a meaning that is consistent with their meaning inthe context of the relevant art and the present disclosure. Suchcommonly used terms will not be interpreted in an idealized or overlyformal sense unless the disclosure herein expressly so definesotherwise.

Where used in this disclosure, the term “computer” is meant to encompassa workstation, personal computer, personal digital assistant (PDA),wireless telephone, or any other suitable computing device including aprocessor, a computer readable medium upon which computer readableprogram code (including instructions and/or data) may be disposed, and auser interface. Terms such as “server” and “client”, and the like areintended to refer to a computer-related entity, including hardware or acombination of hardware and, software. For example, an engine may be,but is not limited to being: a process running on a processor; aprocessor including an object, an executable, a thread of execution,and/or program; and a computer. Moreover, the various computer-relatedentities may be localized on one computer and/or distributed between twoor more computers. The terms “real-time” and “on-demand” refer tosensing and responding to external events nearly simultaneously (e.g.,within seconds, milliseconds, or microseconds) with their occurrence, orwithout intentional delay, given the processing limitations of thesystem and the time required to accurately respond to the inputs. Theterms “near real-time” and “near on-demand” also refer to the sensingand responding to external events that may be close to simultaneous, ornear simultaneous, (e.g., within hours, minutes) with their occurrence,or without intentional delay, given the processing limitations and dataflow capacity of the system to accurately respond to the inputs. Theterms “real-time” and “near real-time” depend on the system and thecapacity provided to the system for processing and data movement. Theyshould in no way limit the scope in which the invention is presentedherein.

Programming Languages

Embodiments of the present invention can be programmed in any suitablelanguage and technology, such as, but not limited to: AssemblyLanguages, C, C++; C#; Python; Visual Basic; Java; VBScript; Jscript;Node.js; BCMAscript; DHTM1; XML and CGI. Alternative versions may bedeveloped using other programming languages including, Hypertext MarkupLanguage (HTML), Active ServerPages (ASP) and Javascript. Any suitabledatabase technology can be employed, such as, but not limited to,Microsoft SQL Server or IBM AS 400, as well as big data and NoSQLtechnologies, such as, but not limited to, Hadoop or Microsoft Azure.Referring now to the attached Figures, embodiments of the presentinvention will be more thoroughly described.

FIG. 1 shows a centralized system 100 that illustrates the passiveagricultural geospatial data collection and transfer for the generationof actionable management information and insights, which in particularembodiments, includes a work machine 101 communicably coupled via acloud enabled network 110, to a guidance system 112, e.g., in a server111/client 113 architecture as shown. The system 100 containsagricultural equipment 101 with an equipment system bus 102, e.g., inthe form of a conventional Controller Area Network (CAN) bus, thatallows interaction with the different systems on-board that control,operate, and monitor the equipment. Also connected to the equipmentsystem bus 102 are a GPS receiver system 104, a data collection device105, and a wireless data transfer device system 106. A GPS receiversystem 104 connected to the equipment bus system 102 allows for GPSsignals 108 being transmitted via GPS satellites 107 to be received andsent to the equipment bus 102 for GPS-based positioning control of theequipment. The GPS receiver system 104 also allows for the GPS signaldata 108 to be combined/matched with the equipment system bus data 103which creates agricultural geospatial data 109, that may contain butshould not be limited to, GPS positioning data, temporal data, machineand equipment data, agronomic data, monitor/controller data, or anyother equipment sensor data. The passive agricultural geospatial datacollection device 105 may be connected wirelessly or through a physicalconnection to the equipment system bus 102 as well as to the datatransfer device system 106. The passive agricultural geospatial datacollection device 105 may monitor the equipment system bus 102 withoutinterfering with the operation of the equipment system bus 102 whilealso measuring the agricultural geospatial data 109 that is transmittedto the different systems. The agricultural geospatial data 109 may thenbe transferred, via the transfer device system 106, to a cloud enablednetwork 110. Once the agricultural geospatial data 109 has beentransferred to the cloud enabled network 110, the data 109 can then betransferred to a server 111 in which an adaptive data analysis algorithmcan be completed. The result of the adaptive data analysis algorithm canthen be sent, via a communication network 110, to a client 113 for thedynamic classification algorithm to be completed and actionablemanagement information and insights to be generated.

In alternate embodiments of the data collection device 105, the device105 may both monitor the equipment system bus 102 and measure theequipment system bus data 103 without the GPS signal data 108. The datacollection device 105 may contain a GPS receiver system 104 within thedevice system 105 so that GPS signal data 108 may be directly measuredby the data collection device 105 and combined/matched with theequipment system bus data 103 at the data collection device 105 beforetransfer of the agricultural geospatial data 109 by the transfer devicesystem 106 occurs. Data collection device 105 may also contain thetransfer device system 106 within itself, so that agriculturalgeospatial data 109 may be collected and transferred by the same device105. The data collection device 105 may also have the ability tointeract with the equipment system bus 102 to control specific systemsresiding on the bus for input from external controlling systems. Itshould also be recognized that a combination of this system may have notbeen discussed within this explanation, but known that an alternateembodiment of the description may be possible while still representingthe concepts and figures presented herein.

In another alternate embodiment, the data source that is connected tothe cloud enabled network 110 and to the server 111 may not comedirectly from the data collection device 105 on the agriculturalequipment 101. The agricultural geospatial data 109 may be from amultitude of different sources that have historically collectedagricultural geospatial data 109 or are connected via cloud/webapplication data services. These sources may include, for example,portable storage media such as USB flash drives or external hard drives,web application data services, and data storage databases. In variousembodiments of potential agricultural geospatial data 109 storage, aconnection to the server 111, through a cloud enabled network 110 orphysical connection, is made and becomes a part of the centralizedsystem 100 shown.

FIG. 1 also shows just an example of a simplified, and specific,embodiment of an agricultural telematics system 100 that is based on aclient-server architecture. This by no means, should limit the conceptsherein, as other potential embodiments of this telematics system 100 mayinclude configurations of, just a server for all processing, just aclient for all processing, a combined server-client unit, or any type ofarchitecture that allows the processing and analyzing of data to flowfrom agricultural equipment 101 to farm managers.

FIG. 2 , then, shows an equipment system bus 102 with a more detailedlevel 200 of the on-board systems associated with agricultural equipment101. The system contains a centralized data bus 102 that allows thetransfer of data 103 109 to and from different systems contained on theagricultural equipment 101. The equipment system bus 102 may be one, ora combination of data buses that should not be limited to, a controllerarea network (CAN) like CAN bus and ISO bus, Local Interconnect Network(LIN), Ethernet, Transmission Control Protocol (TCP)/Internet Protocol(IP), RS232, CCD, Universal Serial Bus (USB) or any other connectionbetween the equipment operating systems that can transfer data back andforth to the controlling systems.

Systems such as the engine 201, transmission 202, electrical 203,hydraulic 204, hitch 205, power take-off (PTO) 206, and themonitor/controller system 207, as well as the virtual terminal 208, anyimplement ECU systems 209, implement sensor systems 210, and implementcontroller systems 211 are examples of what may be connected to thecentralized equipment bus 102 but in no way should be limited to onlythese. The functionality of this data bus 102 allows for data 103 109flow between systems so proper functionality of the agriculturalequipment 101 may occur. The data 103 109 passed through this bus 102can originate or be received from the different systems through astandard data flow protocol for agricultural equipment 101. It alsoprovides the opportunity for a data collection device 105 to be used togather the geospatial data 109, or just the equipment system bus data103, and then send it to a cloud enabled network 110 using a datatransfer device 106 for further analysis. The data collected 103 109 maycomprise of information from one or multiple systems provided in 200 aswell as systems that may not be visualized within this diagram. The datacollection device 105, once again, may be a passive non-intrusive devicethat connects to existing systems or directly to an equipment system bus102 to record information of the equipment operating parameters, as wellas a device that can provide input to the equipment system bus 102 forcontrol of specific systems within or on the bus.

FIG. 3 shows this collection in a simplified diagram of a system 300that includes multiple pieces of agricultural equipment 101 of multipletypes 301, including substantially any type of work machine operatingsubstantially any type of farm implement. A representative(non-exclusive) list of work machines usable with these embodimentsincludes: Tractors, Combines, Powered Applicators (sprayers, spreaders,floaters), Bulk Harvesters (self-propelled forage harvesters, cottonharvesters, sugar beet harvesters, etc.), Self-Propelled Windrowers andSwathers, Skid-Steer Loaders, Semi-Trucks, Utility Vehicles, andPassenger Vehicles (for farm use—such as pickup trucks). Arepresentative (non-exclusive) list of farm implements (tools) usablewith these embodiments includes: Tillage Tools, Planters, Air Seeders,Drills, Balers, Grain Carts and Bulk Storage Carts, Forage PreparationTools (Conditioners, Rakes, Tedders, etc.), Pull-type Applicators (pulltype fertilizer spreaders or sprayers), Trailers/Wagons/Seed Tenders,Pull-type Harvesters (forage harvesters that are pulled), and ManureSpreaders.

FIG. 3 also shows the flow of agricultural geospatial data 109 fromcollection to the generation of actionable management information andinsights (also referred to herein as “management insight(s)”,“actionable insight”, or simply “actionable information”) 308. It shouldbe noted that system 100 may automatically determine the type ofequipment or implement being used, based on the data captured by datacollection device 105, and/or by use of an implement sensor 115communicably coupled to device 105. FIG. 3 further shows the transfer309 of this actionable information and insights 308 to the farmmanagers/working machine operators 310, which may take the form ofinformation displays as well as operational directives sent by system100. As discussed in detail hereinbelow, examples of actionableinformation include the amount of time the working machine was idleduring performance of various geospatial events, the costs per acreassociated with various geospatial events, correlation of speed ofworking machine to costs associated with various geospatial events, costsavings per acre as a function of speed of the working machine, andcombinations thereof. An exemplary operational directive includesinstructions to increase or decrease the speed of the working machine tomatch an optimal speed generated by the model for a particulargeospatial event. Other exemplary operational directives may include,but should not be limited to: instructions to shift gears of a machineand reduce engine speed through throttle adjustment of the engine inorder to reduce fuel use rate, to reduce fuel cost for a particulargeospatial event; instructions to optimize field efficiency of aparticular geospatial event by providing instructions on obtaining anoptimal work direction of travel within the field boundary, such asshown and described in the above-referenced U.S. patent application Ser.No. 15/490,791; or providing instructions to turn the machine off duringnon-productive times to reduce fuel cost and cost of operation when themachine is not being productive. As these operational directives havebeen explicitly stated, it should be known to those skilled in the artthat this is only a small subset of the potential operational directivesthat could be generated for machine control for a particular geospatialevent. For example, parameters such as engine speed, engine load,distance travelled, fuel usage, as well as other machine, agronomic, ormonitor/controller data may be used to generate these operationaldirectives.

In this regard, embodiments of system 100 are configured toautomatically determine the particular geospatial event being performed,and to provide the operational directive, in real-time, or nearreal-time. The geospatial data 109 collected from the set ofagricultural equipment 301 is connected to the cloud enabled network 110that is connected to a server 111. This server 111 contains a database302 of the agricultural geospatial data that is collected from the datacollection device 105, e.g., including location data captured from GPS104, and operational data captured from the CAN bus of the work machine101, as well as a database 303 that includes geospatially locatedgeometries, e.g., field geometries, for the operating farm. The server111 also contains an adaptive data analysis algorithm 304 that processesand analyzes the agricultural geospatial data 109 from the telematicsgeospatial data database 302 using the geospatially located geometriesdatabase 303, as a reference, to create the summarized agriculturalgeospatial data events 305 for the operational, travel, and ancillarycategories. These summarized data events 305 may then be transferred,via a communication network 110, to a client 113 for further processing.

In embodiments, the communication network 110 may be, but should not belimited to, a wireless or wired network, that may include networks suchas, Local Area Network (LAN), Wide Area Network (WAN), the Internet, anEthernet connection, Universal Serial Bus (USB), Wi-Fi, or Bluetooth forexample. Moreover, the client 113, in this embodiment, may include anydevice that contains a processor and or a means of viewing information,for example, but should not be limited to, devices such as, a laptop ordesktop computer, a smartphone, or a tablet.

In this specific embodiment, as depicted in FIG. 3 , the client 113receives the summarized agricultural geospatial data events 305 whichmay then be dynamically classified, i.e., matched, using the dynamicclassification algorithm 307 in relation to an agricultural operationsmodel 306. As the dynamic classification algorithm 307 classifies theactual geospatial data events 305 the algorithm 307 also generatesactionable management information and insights 308 that may then betransferred, via a transfer method 309, to the farm managers 310 forquick and easy access in order to help make timely and efficientmanagement decisions.

As an alternate embodiment of FIG. 3 , and specifically of server 111,the agricultural geospatial data 109 may also be transferred via thecloud enabled network 110 directly to the adaptive data analysisalgorithm 304 for analysis before storage in the agricultural telematicsgeospatial database 302. In such an embodiment, agricultural geospatialdata 109 may once again come from a multitude of external sources and/oragricultural equipment 101.

A factor in evaluating agricultural geospatial data 109 is not onlybeing able to associate the geospatial data 109 with the properagricultural equipment 101 but also to associate that equipment 101 withits geospatial location in regards to geospatial geometries such as,field boundaries and significant locations associated with a farm. FIG.4 provides a visualization of multiple geospatial field boundaries 401,along with a significant location that has been marked by the farm andis designated as 402. Significant locations 402 may represent, butshould not be limited to, staging areas, storage areas, or any locationthat has been marked as geospatially significant to the farm operationin one way or another. Collectively these geometries create a set ofgeospatial data geometries 400 that can be referenced as a whole farm.This set of geometries 400 play a role in being able to properly locate,attribute, and adaptively classify the geospatial data 109 collectedfrom equipment 101 into operational data of field boundary 401, travelto or from field boundary 401 or location 402, or into ancillary data offield boundary 401 or location 402, for example. These geometries 400are captured by system 100, such as by uploading a computer file, suchas a map, containing the geometries 400 therein. Alternatively, system100 may use the GPS receiver 104 to capture GPS coordinates of theboundaries and topographical features of a field as the agriculturalwork machine 101 traverses the field, e.g., as the machinecircumnavigates the field(s).

FIG. 5 also shows the farm with the set of geospatial geometries 400 butnow with equipment operation on those field boundaries 500. The workoperations that equipment 101 _(0 to n) are performing may all occur atthe same time, individually, or in any combination of occurrence inregards to time and location. FIG. 5 also shows the data collection ofthe operating equipment 101 in all of the field boundaries 401 _(0 to n)transferring each equipment's 101 individual agricultural geospatialdata 109 to the cloud enabled network 110 for further processing.

This further processing may occur on the server 111 and can be shown ingreater detail in the block diagram in FIG. 6 . The server system 111contains the agricultural telematics geospatial data database 302, thegeospatial geometry database 303, a processor 601 that may be able toperform algorithmic computer code/instructions, which contain theadaptive data analysis algorithm 304, and the summarized agriculturalgeospatial data events 305 that are created from the adaptive dataanalysis algorithm 304. Within the processor 601, the agriculturalgeospatial data 109 from the database 302 along with the geospatiallylocated geometries 303 flow together into the agricultural dataself-classification algorithm 602 so the agricultural geospatial data109 may be parsed into operational, travel, and ancillaryclassifications for every field 401 and/or location 402. The data canthen be aggregated and summarized in the geospatial data event creationalgorithm 603 in order for the data events 305 of every field boundary401 and/or location 402 to be created.

In an alternate embodiment of server 111, agricultural geospatial data109 may be directly fed into the adaptive data analysis algorithm 304from the communication network 110. In this way the agricultural datawould flow just with the geospatial geometries 303 into theself-classification algorithm 602 e.g., without first being stored indatabases 302, 303. The incoming data 109 may then again be parsed andclassified into operational, travel, and ancillary categories for allgeospatial geometries stored in 303. At this point, the classified datamay then be stored in agricultural database 302. Other alternateembodiments of server 111, with a restructured path of data flow andstorage may also be recognized by those skilled in the art. While thelocation of data flow and storage may differ along the data pipelinethan what is shown in the figures or otherwise presented herein, itshould not limit the scope of the invention as set forth in the claimshereof.

In particular embodiments, this process can yet further be defined inFIG. 7 which shows a more detailed block diagram of the adaptive dataanalysis algorithm 304. It contains the methods of the agricultural dataself-classification algorithm 602 to adaptively analyze the agriculturalgeospatial data 109 from the agricultural geospatial database 302. Instep 701 the agricultural geospatial data is parsed by the geospatiallylocated geometries 400 (field boundaries 401 or locations 402) providedfrom the database 303 in order to develop the geospatial relationshipbetween the location of the agricultural data 109 and the location ofthe geospatial geometries 400. This relationship is developed bycomparing the location of the agricultural geospatial data 109 with thelocation of the geospatial geometries 400. The comparison of locationdetermines if the data 109 lies within the geospatial boundary of one ormultiple of the geometries in the set 400, if the data 109 resides onthe outside of all geospatial boundaries of the set 400, or if the data109 intersects the geospatial boundary of one or multiple geometries inthe set 400. As the relationship analysis between the agriculturalgeospatial data 109 and the geospatial geometries 400 is completed, aquantitative analysis is also being performed to quantify the geospatialdistance between said agricultural geospatial data 109 and thegeospatial geometries 400 for each said relationship that is developed.

After these geospatial relationships have been developed, the data thenself-classifies itself into operational, travel, or ancillary categoriesin step 702. In this step the data, itself, again provides one of itscollected parameters, which is the moving speed of the equipment, inorder to relate the agricultural geospatial data 109 to an operatingspeed range and/or a travel speed range. Using the geospatialrelationship, as well as the quantified geospatial distance, developedin step 701, along with the speed of movement relationship developed in702, the agricultural geospatial data can self-classify itself into saidcategories. The relationships that the data 109 provides also providethe information to assign the agricultural geospatial data 109 to theproper geospatial geometry 401 402 in the set of geometries 400 for thegiven geometries located in database 303.

Agricultural geospatial data relationships developed from step 702 mayinclude, for example, self-classification of the operational datacategory if the data resides in the boundary of a geospatial geometry401 and the speed of the equipment is within the operating speed range,or self-classification of the travel data category if the data isoutside of the geo spatial geometry 400 and the speed of the equipmentis within the travel speed range. These two examples of relationships,are discussed here to represent what the relationships may contain andin no way should limit the scope by which these relationships are built.It is known that many other relationships are common and possible butare just not discussed herein.

In specific embodiments of the agricultural data self-classificationalgorithm 602, the processes in steps 701 and 702 can be simplified fora specific explanation, in terms of one agricultural geospatial datapoint that contains a latitude and longitude coordinate, and onegeospatial field boundary which contain a series of latitude andlongitude coordinates that make up a boundary when that series isconnected. The data point also contains information such as timestamp(time the data point was collected), and collected parameters, such asspeed of the machine, engine load, fuel usage, etc. Theself-classification process relates the location of the data point tothe location of the field boundary, i.e., does the point lie within theboundary, is it outside of the boundary, and how close is it to theboundary edge. It then takes this relation and performs a similarrelation with the moving speed of the equipment that is associated withthe data point in order to relate it to an operating speed range and/ora travel speed range. When the relationships have been developed thedata point can properly classify itself based on where it was locatedand how fast the equipment was moving at the time of data collection.

In terms of the specific embodiment displayed in FIG. 7 , the dataclassification categories may be described as follows. Data 109 that isclassified as operational, may represent the geospatial data 109 that isperforming an agricultural task for a field boundary 401. Travel datamay represent the data 109 that occurs when agricultural equipment 101is moving from one geospatial geometry 401 402 to another geospatialgeometry 401 402 in order to perform operational or ancillary work. Theancillary data then, may represent the geospatial data 109 that supportseither travel or operational data and may be related to either a fieldboundary 401 and/or a location 402. An example of each of theseclassification types may be, but should not be limited to, operationaldata of an agricultural tractor and tillage equipment 101 performingtillage on a field boundary 401, a self-propelled sprayer 101 travellingfrom one field boundary 401 to another field boundary 401 in order tospray the next field, and finally ancillary data representing anagricultural tractor 101 connecting to an implement, like a tillagetool, in a barnyard 402 for example. These examples are meant to showwhat each data classification may represent, but in no way should betaken as limiting, as many other examples of each data classificationexist, which will be apparent to those skilled in the art in light ofthe disclosures herein.

After the data 109 has been classified as operational, travel, orancillary in 702, the data may then be transferred to the geospatialdata event creation algorithm 603. This algorithm 603 contains threepaths for the data to follow, which include one path for each dataclassification category; operational which starts at 703, travel at 704,and ancillary at 705. The skilled artisan should recognize that althoughthree paths are shown and described herein, greater or fewer numbers ofpaths may be provided without departing from the scope of the presentinvention. The path specified for operational data starts with theprocess of aggregation 703 for all of the previously classifiedoperational data. Using the timestamps of the data, when the data wasinitially measured and recorded from the agricultural equipment 101,along with a temporal analysis approach, the data can be divided,organized, and then aggregated for the creation of geospatial dataevents. This is an automated process, and allows the data to, onceagain, organize itself based on its own collected parameters. In thiscase, it is the time of data collection that allows the data to thengroup itself so that specific geospatial data events may be created.

In more detail, step 703 may start by analyzing the timestamp parameterof the classified operational data for one piece/group of agriculturalequipment, which we'll call 101 ₀ for explanation purposes, which hasbeen assigned to one geospatial geometry, which we'll call 401 ₀ alsofor explanation purposes. Analyzing data for agricultural equipment 101₀ on one geospatial geometry 401 ₀ allows for a direct timestampcomparison which provides a temporal density measurement of the data.This temporal density measurement then allows for gaps in time to beidentified so the data can be partitioned at the identified gaps andaggregated in-between for geospatial data event creation. This procedureallows the data to dictate the number of geospatial data events createdfor the given agricultural equipment 101 ₀ on the given geospatialgeometry 401 ₀. This process 703 may then continue for each and everypiece of equipment 101 _(1 to n) on that geospatial geometry 401 ₀, andthen starts again for the next geospatial geometry 401 ₁ in the listuntil all geospatial geometries 400 contained in the database 302 havebeen analyzed.

In an alternate embodiment of 703, the data that is incoming and isdirectly classified in 702 may then also directly be sent to 703 foraggregation. In this embodiment, the temporal analysis using thetimestamps of the classified data is still used, but instead of using atemporal density to identify separations in time so aggregation of thedata may occur, the timestamps of the data are analyzed in comparison tothe last recorded geospatial data event. In this way, the temporalcomparison to the previous geospatial data event, and the correspondingtimestamps of the classified data that make up the geospatial dataevent, can evaluate if a large enough gap in time has occurred to eithercreate a new geospatial data event, or continue to aggregate theincoming data to the previous geospatial data event. This process,again, can then be applied for all agricultural equipment 101 _(1 to n)and all geospatial geometries 400 that are contained within the database302.

After the geospatial data is aggregated into operational data events in703, the events can then be summarized to provide the operatingcharacteristics of the events in 706. This step 706 of the algorithm 603uses the aggregated data for each event from 703 and summarizes all ofthe data for each measured parameter that was collected by thecollection device 105. A few examples of these measured parametersinclude, but should not be limited to, speed of the agriculturalequipment for the operation event, total time of the operation event,distance travelled during the operation event, and fuel used during theevent. Particular embodiments may also include parameters such as,seeding rate during the event, application rate during the event,average yield during the event, harvest moisture data during the event,or any other machine, agronomic, or monitor/controller data parameterthat may be collected by the data collection device 105.

This aggregation method 603 may also be completed for the agriculturalgeospatial data 109 that has been classified as travel data. Thisprocess, again, begins with aggregating the travel data for eachpiece/group of equipment 101 for every field boundary 401 or location402. Using the timestamps of the geospatial data and a temporal analysismethod, travel data events can be created. These travel data events maycontain all of the relevant measured parameters in the data that theoperational data contained, such as, but should not be limited to, speedof the agricultural equipment during the event, distance travelledduring the event, and total time of the event. This data however, onceaggregated into events, may also contain the travel event origin, orwhere it departed, as well as the destination, where the travel eventarrived. These origins, and destinations, may be, but should not belimited to, field boundaries 401, staging areas, storage areas, or anylocation that has been marked as significant to the farm 402. After thetravel events have been created, step 707 summarizes each event with thesame technique used in 706 in order to provide the operatingcharacteristics associated with each travel event.

The third path the geospatial data event creation algorithm 603 is foragricultural geospatial data 109 that has been classified as ancillary.This data can once again be aggregated by equipment 101 for eachlocation 402 that is designated as a support site for the operation.These aforementioned locations may also include a geospatial boundaryand/or location 402 so that the ancillary data can be classified andaggregated in step 705. The aggregation technique as well as thesummarization technique are the same that are used for the operationaldata in 703, and travel data in 704. Operating characteristics for eachancillary event may then be created by the summarization technique in708 so the next step 709 of the adaptive data analysis algorithm 304 maybe completed.

The aggregation of the classified geospatial data for the creation ofagricultural geospatial data events helps to provide another layer ofcontext, and therefore, usefulness to the data that is being processed.The classification methods used in the self-classification algorithm602, provide the data with context to which geospatial geometry 400 itbelongs, as well as, which classification category it is. Aggregatingthis data then provides another layer of context which can be thought ofas the geospatial data event layer. This layer of context allows for thesummarization of the classified data in order to describe eachindividual geospatial data event. As farms make decisions on thesegeospatial data event types, the ability to form the context into alayer that is easily relatable farm managers 310 is important to makethe agricultural geospatial data 109 useful, actionable, and alsobeneficial for further context to be built upon.

The summarizations of the operational 706, travel 707, and ancillary 708data events then allow for the creation of a chronological list of theseevents for each and every field 401 and/or location 402 as they occurredin time. In terms of the operational data events, this process 709starts by sorting the time of occurrence of each data event for a givengeospatial geometry 401 ₀, which would include all operational eventsfor all agricultural equipment 101 _(0 to n). After the time seriessorting, the list provides an order of operational data events for thegeospatial geometry 401 ₀, that we can call field events or fieldoperation events, which occurred in chronological order and contain allof the associated agricultural equipment 101 _(0 to n) that performedthe work. This process 709, can then be repeated again for all fields401, and locations 402, until each geospatial geometry 400 contains alist of field events 305. These lists can then be used and transferred,via a communication network 110, to a client 113 for further processingas depicted in FIG. 6 .

The same process 709, may then be repeated for travel data events andancillary data events. Ancillary data events may use locations 402,which have been set up by the farm as significant, to order all of theequipment's ancillary operations as they occurred. Travel data events,on the other hand, are slightly different in that they may use the fieldboundary 401 and/or location 402 as a place that travel eitheroriginated from or arrived to, and can be ordered and listed for everyfield boundary 401 and/or location 402 in that manner.

FIG. 8 displays an example of a summarized agricultural geospatial dataevent list 305 that has been classified as operational and was generatedthrough the above process for an individual field 401 ₀. This data eventlist 305 contains not only the equipment the event corresponds to, butalso the summarized operating characteristics that are obtained throughthe algorithmic processing of the adaptive data analysis algorithm 304.It should be noted that the geospatial data event list 305 containsinformation such as, total pass time, total distance covered, averagespeed, fuel used, and average engine load, but should not be limited tothese summarization parameters as many others may be collected.

As FIG. 8 displays a simplified example of a summarized agriculturalgeospatial data event list 305, it may be realized that an alternateembodiment of this list may be a database that contains all of thesegeospatial data event lists 305. This database may contain all of thesame information as displayed in the geospatial data event list 305 andmay be used to query the data contained within for the furtherprocessing in client 113.

With that alternate embodiment realized, FIG. 9 gives a more detailedview of the client 113, which inputs the summarized agriculturalgeospatial data events 305 as shown in FIG. 8 that have been transferredvia the communication network 110. The client 113 may contain anagricultural operations model 306, a processor that may be able toperform algorithmic computer code/instructions 901, along with thedynamic classification algorithm 307, and the actionable managementinformation and insights 308 generated by the algorithm 307. The dynamicclassification algorithm 307 can also be divided into two pieces; theagricultural geospatial data event classification algorithm 902, and theinformation and insights generation algorithm 903. The agriculturalgeospatial data events 305 along with results from the agriculturaloperations model 306 are used within the data event classificationalgorithm 902 in order to classify each event that occurred within thefield boundary 401 and/or location 402. Once the classification of thegeospatial data events 305 have been classified in step 902 the events305 are then provided to the information and insights generationalgorithm 903 in order for actionable information and insights 308 to begenerated.

In an alternative embodiment, the agricultural operations model 306 mayinstead be fully contained within the server 111 portion of the system,or the agricultural operations model 306 may also be a hybrid modelwhere part of the model 306 is contained within the server 111 and partof the model 306 is contained within the client 113. Furthermore, theagricultural operations model 306 may also be structured as a databaserather than a model. In this potential embodiment, information containedwithin the agricultural operations model 306 may be stored in thedatabase and then used to help classify the agricultural geospatial dataevents 305 using the algorithm 307. In either of the potentialconfigurations or structure of the agricultural operations model 306, itperforms the same task in the system and is used to help generateactionable information and insights 308.

In order to do this, the agricultural operations model 306, which can beseen in more detail in FIG. 10 , may create a planned, e.g., idealized,and optionally chronological, list of equipment operation events 1004for every geospatial geometry 400. These geometries 400, may be from thegeospatial geometry database 303, may come from a separate source, ormay be a combination of the two. This model 306 may then use informationsuch as a data collection device list 1001 along with an equipment list1002 for the farm so that equipment 101 may be associated with theproper data collection device 105 in order for known devices 105 andequipment 101 to be used to create equipment operations 1003. Otherplanned information that may be associated with the equipment operations1003 include, but should not be limited to, equipment cost information1005, fuel cost information 1006, and labor cost information 1007 inorder for cost parameters to be measured and associated with theoperation events. These planned factors may then help in devising aplanned chronological list of equipment operation events 1004. Anexample of a planned chronological list of equipment operation events1004 can be seen in FIG. 11 . Similar to the geospatial data events list305, shown in FIG. 8 , it may contain information such as the operatingequipment 101 and the operation event name, as well as the summarizedcharacteristics created from the model 306. FIG. 11 shows a small subsetof summarized characteristics for simplicity purposes, but it should beunderstood, that parameters such as, which should not be limited to,equipment cost, fuel cost, labor cost, total cost, downtime, and fieldefficiency may also be contained along with others.

In an alternate embodiment, the agricultural operations model 306 maynot create a planned chronological list of equipment operation events1004 for each field boundary. Instead the equipment operations 1003 mayprovide the necessary information to the dynamic classificationalgorithm 307 itself, so that actionable management information andinsights may be created.

Also, in a similar fashion to the potential embodiment of a database forthe agricultural geospatial data events 305, the chronological plannedlist of equipment operation events 1004 may also potentially reside in adatabase structure. This would, once again, contain all of theinformation and parameters that the planned list 1004 would contain andwould perform the same function in the system to allow the dynamicclassification algorithm 307 to classify/match the actual geospatialdata events 305 with planned events 1004.

FIG. 12 shows a more detailed flowchart of the dynamic classificationalgorithm 307 as described above that contains the agriculturalgeospatial data event classification algorithm 902, and the informationand insights generation algorithm 903. The planned chronological list ofequipment operation events for each field boundary 1004, along with thesummarized agricultural geospatial data events 305, are used to compareagainst one another using their similar summarized characteristics instep 1201 of the data event classification algorithm 902. Based on howclosely the summarized characteristics of the planned list events 1004and the summarized events 305 are, the data event classificationalgorithm 902, then classifies each event for each field in 1202. Theclassifications of these events may be, but should not be limited to, anevent found in the summarized geospatial data events 305 was matched toan operation event listed in the planned list of operation events 1004,an event from the geospatial events 305 was unable to be matched to anevent in the planned list 1004, and the event 305 was unable to be foundwithin the planned list 1004. This last classification may distinguishitself from the middle classification in terms of an example, in whichan event 305 has been generated for an agricultural piece of equipment101 with a data collection device 105 but the equipment 101 was not inthe planned chronological list 1004, resulting in an unknown actualevent, as opposed to, just unable to match an event 305 with an event inthe planned list of 1004.

After the classification of the geospatial data events 305 has occurred,operation event names specified in the planned operation event list 1004may be assigned to the corresponding matched and classified geospatialdata events 305 in 1203. Extended information, resulting from theagricultural operations model 306 such as, but should not be limited to,equipment cost, fuel cost, and labor cost to calculate extended capacityand financial information, may also then be assigned as well. Theinformation generation algorithm 903 may then take the assigned andgeospatial data events 305 and generate actionable managementinformation and insights 308.

In an alternate embodiment of the agricultural geo spatial data eventclassification algorithm 902, methods 1201, 1202, and 1203 that compare,classify and assign the summarized geospatial data events, may insteadconsist of just one step in which the planned list 1004 are assigned tothe summarized geospatial data events 305. These planned geospatialevents 1004 may be assigned through the use of time of occurrence ofboth the planned list of data events 1004 and the summarized geospatialdata events 305 and how they occur chronologically. In an embodimentaforementioned where a planned list of events 1004 was not created, thesingle step method would assign the equipment operations 1003 with thegeospatial data events 305. This assignment process would relate thespecific agricultural equipment 101 with the equipment used in thesummarized geospatial data event 305, along with the time of occurrenceof the event 305, to assign the operation 1003 name and extendedinformation from the agricultural operations model 306.

In either embodiment of the agricultural geospatial data eventclassification algorithm 902, the summarized geospatial data events 305are matched with corresponding information from the agriculturaloperations model 306. The fitting of the summarized geospatial dataevents to the information from the agricultural operations model 306, isanother way to provide further context to the data, which allows forfurther information and insights to be generated. It not only providescontext to the data in which farm managers 310 can easily recognize, butit also provides some technical advantages. Creating context from theagricultural geospatial data 109 from the beginning of the analysissystem 100, to automatically classify into operational, travel, andancillary, then to assign to the proper geospatial data geometry 400,and finally aggregate and summarize the data into geospatial data eventsallows for easy fitting to the agricultural operations model 306. Aneasy fit to the agriculture operations model 306 allows for lowerprocessing time and resources, and increases efficiency in the processesas not all potential outcomes, scenarios, and permutations need to beevaluated in order for a fit of the model 306 to occur. Data storage mayalso be reduced by this technique as not all of these various scenariosneed to be stored for further comparisons and evaluations. Finally, anyfurther aggregation of the summarized geospatial data events 305 mayalso be performed in a very computationally inexpensive manner as theyhave already been contextualized and would just need simple aggregationtechniques performed on the queried data.

This is the case for the information and insights generation algorithm903 as it begins with aggregating the classified/matched/assigned eventsby the different types of agricultural management categories 1204. Thesemanagement categories may contain, but should not be limited to, thebusiness farm entities or clients, land ownership entities or farms,fields, storage locations, staging areas, equipment, operations,laborers, or any other category that may be used to aggregate thesummarized geospatial data events and their characteristics 305. Oncethe classified agricultural geospatial events 305 have been aggregatedinto management category, the aggregated information may be analyzed inorder to identify the most important and influential managementcategories that may provide the most actionable information and insights1205. This step may contain processes in which categories have beenselected prior to analysis to identify the most important informationand influential categories.

With actionable management categories and their relationships obtainedin step 1205, the algorithm 903 can then generate reports,visualizations, actionable information, and insights 1206 in order toextract the significant relationships from the agricultural geospatialdata events 305. The information and insights generated may bedisplayed, for example, in tabular reports, graphed visualizations ofthe geospatial event data, and summary information on both the resultsgenerated as well as the correlated actionable insights that theagricultural geospatial event data 305 may have provided. The actionablemanagement information and insights 308, may also be generated tocontain just the most actionable information and insights using step1205, just all information and insights using the aggregated managementcategory information from 1204, or a combination of both in order toprovide the farm managers 310 with the most actionable and desiredmanagement information and insights 308 as possible.

In an alternate embodiment of the dynamic classification algorithm 307,the algorithm may perform similar steps as described above but in thescenario where one, two, or all three of the geospatial data events 305,the agricultural operations model 306, and the chronological plannedequipment operation events 1004 are in a database structure. In thisembodiment, the databases of the planned operation events 1004 and thegeospatial data events 305 would be compared and matched to each otherusing a similar technique that is described above, but the way the datafrom 1004 and 305 would be accessed may be different as well as theunderlying data structure. The process would still be able to obtain theclassified geospatial data events as well as generate actionableinformation and insights. The databases may contain, but should not belimited to, the same operating characteristics as mentioned above butalso contain extended financial information regarding the associatedcosts of equipment, inputs such as seed, fertilizer, and chemicals, andlabor and may be used to compare, contrast, and align the data withinthe databases to achieve the classification and information and insights308 generation.

It should also be noted here that, the dynamic nature of theclassification algorithm 307 may be attributed to the inputs of thechronological list of operation events 1004 from the agriculturaloperations model 306, the summarized agricultural geospatial data events305, as well as the structure of the algorithm itself. The inputs 1004and 305 may dictate the results of the algorithm 308, by the way thealgorithm 307 uses the information provided to generate the results 308.If new agricultural geospatial data 109 is collected and processed intonew summarized agricultural data events 305, the algorithm may adjust toaccount for these new events 305. In a similar approach, if anyparameter in the agricultural operations model 306 is modified thatchanges a related parameter, in any way, the algorithm may re-adjust forthe new parameters in real-time so new actionable management informationand insights 308 may be generated to reflect the change. These two inputchanges may also occur at the same time in which the new generatedresults 308 may also occur.

With the actionable management information and insights 308 generated,the management information and insights transfer method 309 may thentransfer the information and insights 308 to the farm managers 310 forviewing purposes as depicted in FIG. 9 . The actionable managementinformation and insights transfer method 309 may include, for example, aconnection that may be wireless or wired to a visual screen or monitorwithin the client 113 or to a wireless or wired connection to any devicethat is able to display the information and insights 308. This transfermethod 309 may transfer the information and insights to the farmmanagers 310 and to any number of devices they may use, such as, forexample, mobile phones, desktop or laptop computers, tablets, PDAs,printers, fax machines, or any device that is able to display and/orvisualize the information and insights 308 sent.

FIG. 13 and FIG. 14 display examples of this actionable managementinformation 308 that may be created by the system 100. FIG. 13 displaysthe percentage of total operation time that the equipment was notmoving, or was at 0 mph, for different agricultural operation events.The information provided in FIG. 13 may then show farm managers 310, forexample, which operation events had the largest percentage of downtime,or time at 0 mph, as well as the comparison of the time at 0 mph forsimilar operation events such as Plant Corn and Plant Soybeans. Thisinformation is actionable because it allows the farm managers 310 tomake decisions based on equipment operators, equipment used, or on theequipment operation event itself in order to improve the performance.

In a similar manner, FIG. 14 displays a total cost per acre breakdownfor each operation event in this example. The breakdown includes averageequipment cost per acre, the average fuel cost per acre, and the averagelabor cost per acre for the operation events on all fields for theentire farm. From FIG. 14 , information on the operation events thathave been analyzed through the system 100 provided herein, may includewhich operations cost the most on a per acre basis, which operationshave the most equipment, labor, and/or fuel cost per acre, thecomparison of like operation events such as planting or spraying, andhow much some operation events cost relative to other operation events.All of this information generated is actionable because it helps farmmanagers 310 make decisions on operators, equipment, logistics, or onthe operation events itself, in an attempt to try and limit the cost ofthese operation events.

Once again, the results displayed in FIG. 13 and FIG. 14 are examples ofactionable information 308 that may be generated through the system 100presented herein. These in no way should be taken as limiting examples,but rather, are shown as simplified displays of the actionableinformation generated through the system.

FIG. 15 displays an example of the actionable management information(also referred to herein as “management insight”, “actionable insight”,or simply “actionable information”) 308 that may be generated from thesystem 100. This example should be taken as a simplified and anon-limiting example of what the system presented herein may be capableof providing.

FIG. 15 depicts the actionable insight 308 that may be generated fromthe system 100, in terms of total farm savings in planting andharvesting costs due to increasing the average operation speed. Thefigure is split into four main charts that visualize the correlation ofplanting and harvest speed to the total operation event cost per acre15A, the dollar per acre savings per mile per hour (mph) increase 15B,the speed increase to reach planned speed and the associated dollar peracre savings for that increase 15C, and finally the total potentialsavings in planting and harvest costs all summed up 15D. These fourcharts take the information and generate the insights to allow the farmmanagers 310 to make the actionable decision, in this specificembodiment, of whether the planting and harvesting operation eventsshould be performed at a higher speed. This decision can help be made byevaluating the insight provided from the four figures within the chart.It also allows for farm managers 310 to gain insight on the comparisonof actual operation characteristics versus the planned characteristics,which can be thought of as a set standard. Setting a standard allowsfarm managers 310 to realize operational differences in actual versusthe planned and manage accordingly to achieve that standard. Thefollowing describes an example of this in relation to operation speedand its associated costs.

In 15A the correlation of planting and harvest speed to the total dollarper acre cost can be seen. From the figure, it can be seen that theplanned speed, which can be seen as the thicker gray bar on the left(speed) side of the chart, shows a faster operation event speed thandoes the actual operation event speed. This difference in speed can becorrelated to the total cost per acre on the right ($/acre) hand chartin 15A. The slower speed of operation events show that the total cost ofoperation per acre is higher as opposed to the cost for the plannedoperation event speed. To help quantify this difference in cost due tothe speed of operation, 15B shows the potential savings in total dollarsper acre by a 1 mph average increase in speed of operation. This dollarper acre savings per mph can be seen for the different planting andharvesting operations and shows which of the operations may make themost sense to speed up if possible. With planting corn showing thehighest dollar per acre savings per mph increase, it may make sense asthe farm manager to try and speed up the corn planting operation eventto obtain those potential savings. Whereas, the harvesting cornoperation event may still prove to save money by speeding up theoperation event, the savings may just not be by quite as much as theother operation events shown.

While 15A and 15B present the actionable insights to correlate speed ofoperation with the cost of operation, the lower two charts 15C and 15Dprovide the actionable insights 308 of total cost savings. In 15C, thechart displays the increase in speed of operation to obtain the plannedspeed of the operation event, as well as the dollar per acre savings foreach of the operation event's speed increase. This chart shows that byincreasing the average speed by the given amount, large potentialsavings may be seen in the total cost of operation. This total savingsmay then be rolled up and shown in the donut chart in 15D. This chartdisplays the total operation cost for both planting and harvesting aswell as the potential savings from the total cost of operation. Thechart visualizes that 15% of the total cost of operation may be saved ifthe planned speed of operation for planting and harvesting is achievedduring the actual operation events. This potential 15% in savings wouldamount to $18,514 in savings for this specific embodiment. Driving theinformation provided to the insight of just achieving the plannedaverage speed for planting and harvesting operation events couldpotentially save the farm $18,514. This may provide enough insight todrive the decision of the farm managers 310 to make sure that theoperators of the equipment, for these operating events, achieve thespeed of operation that was set. The insight of increasing speed ofoperation is an actionable decision for farm managers 310 to make andmay allow them to optimize the farming operation events to help themsave money, increase production and efficiency, and ultimately fine tunetheir overall performance of the farm operation.

With the potential advantages presented of increasing speed of operationfor planting and harvesting operations, embodiments of the invention usethis insight to provide the aforementioned operational directive ofcontrolling the work machine 101 to achieve the set speed. Thisoperational directive can be implemented through control of the varioussystems 201-211 communicably coupled to the equipment system bus 102, asshown in FIG. 2 . For example, the engine 201, transmission 202, andelectrical system 203, may be controlled, e.g., through the ECU of thework machine, to allow the work machine 101 to achieve the desiredspeed. This control may be obtained through an automated feedback systemthat allows information generated by the guidance system 112, to be sentin the form of an operational directive via cloud/communication networks110 to the machine 101 in order to control the necessary systems on theequipment system bus 102. The operational directive(s) may also be usedin a more manual, or semi-automated, manner in which the insightgenerated by guidance system 112 is displayed to an operator within themachine 101, so that the operator may implement the directive usingconventional operating controls of work machine 101, e.g., by shiftinggears and adjusting throttle speed. In any case, whether through theautomated process of controlling the machine via equipment system bus102, by operator control, or by some combination thereof, embodiments ofthe present invention allow the operational directive to be received,read, executed, and implemented.

Again, it should be understood that the above example of sending anoperational directive of altering the speed of the machine 101 is justan example and should not be taken as limiting. As other operationaldirectives are acted upon, any number of the systems 201 through 211(FIG. 2 ), as well as other systems that may be developed in the future,may need to be controlled and adjusted in order to achieve the desiredmachine operation.

FIG. 16 shows a diagrammatic representation of a machine in theexemplary form of a computer system 1300 within which a set ofinstructions, for causing the machine to perform methodologies discussedabove, may be executed.

The computer system 1300 includes a processor 1302, a main memory 1304and a static memory 1306, which communicate with each other via a bus1308. The computer system 1300 may further include a video display unit1310 (e.g., a liquid crystal display (LCD), plasma, cathode ray tube(CRT), etc.). The computer system 1300 may also include an alpha-numericinput device 1312 (e.g., a keyboard or touchscreen), a cursor controldevice 1314 (e.g., a mouse), a drive (e.g., disk, flash memory, etc.)unit 1316, a signal generation device 1320 (e.g., a speaker) and anetwork interface device 1322.

The drive unit 1316 includes a computer-readable medium 1324 on which isstored a set of instructions (i.e., software) 1326 embodying any one, orall, of the methodologies described above. The software 1326 is alsoshown to reside, completely or at least partially, within the mainmemory 1304 and/or within the processor 1302. The software 1326 mayfurther be transmitted or received via the network interface device1322. For the purposes of this specification, the term“computer-readable medium” shall be taken to include any medium that iscapable of storing or encoding a sequence of instructions for executionby the computer and that cause the computer to perform any one of themethodologies of the present invention, and as further describedhereinbelow.

Certain aspects of the present invention include process steps andinstructions described herein in the form of an algorithm. It should benoted that the process steps and instructions of the present inventioncould be embodied in software, firmware or hardware, and when embodiedin software, could be downloaded to reside on and be operated fromdifferent platforms used by real time network operating systems.Moreover, the particular naming of the components, capitalization ofterms, the attributes, data structures, or any other programming orstructural aspect is not mandatory or significant, and the mechanismsthat implement the invention or its features may have different names,formats, or protocols.

Moreover, unless specifically stated otherwise as apparent from theabove discussion, it is appreciated that throughout the description,discussions utilizing terms such as “processing” or “computing” or“calculating” or “determining” or “displaying” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system memories orregisters or other such information storage, transmission or displaydevices.

Embodiments of the present invention also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, or it may comprise a computerselectively activated or reconfigured by a computer program stored on acomputer readable medium that can be accessed by the computer. Such acomputer program may be stored in a tangible, non-transitory, computerreadable storage medium, such as, but is not limited to, any type ofdisk including floppy disks, optical disks, CD-ROMs, magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, application specific integratedcircuits (ASICs), any other appropriate static, dynamic, or volatilememory or data storage devices, or other type of media suitable forstoring electronic instructions, and each coupled to a computer systembus. Furthermore, the computers referred to in the specification mayinclude a single processor or may be architectures employing multipleprocessor designs for increased computing capability.

In addition, the present invention is not described with reference toany particular programming language. It is appreciated that a variety ofprogramming languages may be used to implement the teachings of thepresent invention as described herein, and any references to specificlanguages are provided for disclosure of enablement and best mode of thepresent invention.

The present invention is well suited to a wide variety of computernetwork systems over numerous topologies. Within this field, theconfiguration and management of large networks comprise storage devicesand computers that are communicatively coupled to dissimilar computersand storage devices over a network, such as the Internet.

Modifications, additions, or omissions may be made to the systems,apparatuses, and methods described herein without departing from thescope of the disclosure. For example, the components of the systems andapparatuses may be integrated or separated. Moreover, the operations ofthe systems and apparatuses disclosed herein may be performed by more,fewer, or other components and the methods described may include more,fewer, or other steps. Additionally, steps may be performed in anysuitable order. It should be further understood that any of the featuresdescribed with respect to one of the embodiments described herein may besimilarly applied to any of the other embodiments described hereinwithout departing from the scope of the present invention. As used inthis document, “each” refers to each member of a set or each member of asubset of a set.

To aid the Patent Office and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants wishto note that they do not intend any of the appended claims or claimelements to invoke 35 U.S.C. 112(f) unless the words “means for” or“step for” are explicitly used in the particular claim.

What is claimed is:
 1. A machine guidance apparatus for completing farming operations with operational efficiency on a farm having at least one field or significant location, the apparatus comprising: an agricultural work machine configured for traversing the farm, the agricultural work machine having an ECU (Electronic Control Unit) communicably coupled via a system bus to a plurality of machine system elements including an engine and sensors to control and monitor engine functions; a farm implement operationally engaged with, and operated by, the agricultural work machine to effect farming operations as the agricultural work machine traverses the farm; a GPS receiver disposed on the agricultural work machine to generate location data for the work machine; and a data collector communicably coupled to the GPS receiver and to the system bus, the data collector configured to capture agricultural geospatial data including the location data for the work machine from the GPS receiver and engine function data from the ECU; and a guidance system communicably coupled to the data collector, the guidance system having a memory and a processor, wherein the memory includes a stored program executable by the processor, the stored program configured to: (a) capture geospatially located geometries of the farm, including field boundaries and topographical features of the field; (b) use the geospatially located geometries of the farm to mathematically generate a total farmable field area; (c) capture physical parameters of the farm implement, including a dimension transverse to forward movement of the agricultural work machine as the agricultural work machine traverses the farm; (d) capture the agricultural geospatial data; (e) automatically classify the agricultural geospatial data, using the geospatially located geometries of the farm, into one or more activity/event categories including operational events, travel events, and ancillary events, to produce classified data; (f) aggregate the classified data to create a plurality of geospatial data events; (g) match the plurality of geospatial data events to planned versions of said plurality of geospatial data events within an agricultural operations model to generate a plurality of matched events; (h) use the plurality of matched events to generate actionable information for the working machine in real-time or near real-time; and (i) generate and send operational directives to the agricultural work machine based on the actionable information, the operational directives including instructions for altering a direction of travel of the work machine to match an optimal direction of travel generated by the model to optimize field efficiency for an identified geospatial data event, based at least in part on the physical parameters of the farm implement and the total farmable field area.
 2. The apparatus of claim 1, wherein the operational directives for the working machine include: (i) instructions for increasing or decreasing the speed of the working machine to match an optimal speed generated by the model for an identified geospatial data event; (ii) instructions for shifting gears and reducing engine speed to match optimal levels generated by the model for an identified geospatial data event; and/or (iii) instructions to turn off the working machine during non-productive times.
 3. The apparatus of claim 2, wherein the guidance system is configured to generate and send operational directives to the agricultural work machine in real-time or near real-time.
 4. The apparatus of claim 1, wherein said automatically classify (e) comprises mapping the agricultural geospatial data to the geospatially located geometries of the farm to determine whether the agricultural geospatial data references a location (i) within any of the field boundaries, (ii) outside any of the field boundaries, or (iii) at an intersection of any of the field boundaries.
 5. The apparatus of claim 4, wherein said automatically classify (e) further comprises determining whether the agricultural geospatial data references speed of the equipment within an operating speed range, or within a travel speed range.
 6. The apparatus of claim 5, wherein said automatically classify (e) further comprises determining a type of work machine and/or implement used to generate the agricultural geospatial data.
 7. The apparatus of claim 6, further comprising an implement sensor disposed on the work machine, configured to capture the type of work machine and/or implement.
 8. The apparatus of claim 1, wherein the agricultural geospatial data further comprises temporal data; machine and equipment data including speed of the machine, engine load, fuel usage; agronomic data; monitor/controller data; equipment sensor data; and combinations thereof.
 9. The apparatus of claim 8, wherein said location data provides a travel path of the work machine.
 10. The apparatus of claim 1, wherein: the operational events comprise the work machine performing agricultural tasks within field boundaries; the travel events comprise the work machine moving from one field to another field; the work machine moving from one significant location to another significant location; and combinations thereof; and the ancillary events comprise the work machine engaging in operations supportive of the operational events and/or the travel events.
 11. The apparatus of claim 10, wherein the operational events are selected from the group consisting of: planting; applications such as spraying or spreading; harvesting; tilling; grain or bulk crop carting; grain or bulk crop hauling; baling; seed tending; and preparation work such as swathing, windrowing, conditioning, raking, or tedding.
 12. The apparatus of claim 11, wherein the ancillary events include idling and switching implements.
 13. The apparatus of claim 1, wherein the actionable information is selected from the group consisting of: percentage of time the working machine spent idling during various ones of said geospatial data events; costs per acre associated with various ones of said geospatial data events; correlation of speed of working machine to costs associated with various ones of said geospatial data events; cost savings per acre as a function of speed of the working machine; and combinations thereof.
 14. The apparatus of claim 1, wherein the engine function data from the ECU includes speed, fuel use and engine rpm/load data.
 15. The apparatus of claim 1, wherein said capture (a) comprises using the GPS receiver to capture GPS coordinates of the boundaries and topographical features of a field as the agricultural work machine traverses the field.
 16. The apparatus of claim 1, wherein said capture (a) comprises capturing coordinates of the boundaries and topographical features of a field from a map.
 17. The apparatus of claim 1, wherein said capture (d) further comprises capturing a speed at which the farm implement is traversing the farm.
 18. The apparatus of claim 1, wherein said aggregate (f) further comprises placing the plurality of geospatial data events into management categories.
 19. The apparatus of claim 18, wherein the management categories are selected from the group consisting of: business farm entities or clients, land ownership entities or farms, fields, storage locations, staging areas, equipment, operations, laborers, and combinations thereof.
 20. A method for completing farming operations with operational efficiency on a farm having at least one field or significant location, the method comprising: providing an agricultural work machine configured for traversing the farm, the agricultural work machine having an ECU (Electronic Control Unit) communicably coupled via a system bus to a plurality of machine system elements including an engine and sensors to control and monitor engine functions, the agricultural work machine including a GPS receiver, a data collector, and a specialized guidance system including a memory and a processor, the memory including a stored program executable by the processor; operationally engaging and operating a farm implement with the agricultural work machine; capturing, with the data collector, agricultural geospatial data including location data for the work machine from the GPS receiver and engine function data from the ECU; and executing the stored program to: (a) capture geospatially located geometries of the farm, including field boundaries and topographical features of the field; (b) use the geospatially located geometries of the farm to mathematically generate a total farmable field area; (c) capture physical parameters of the farm implement, including a dimension transverse to forward movement of the agricultural work machine as the agricultural work machine traverses the farm; (d) capture the agricultural geospatial data; (e) automatically classify the agricultural geospatial data, using the geospatially located geometries of the farm, into one or more activity/event categories including operational events, travel events, and ancillary events, to produce classified data; (f) aggregate the classified data to create a plurality of geospatial data events; (g) match the plurality of geospatial data events to planned versions of said plurality of geospatial data events within an agricultural operations model to generate a plurality of matched events; (h) use the plurality of matched events to generate actionable information for the working machine in real-time or near real-time; and (i) generate and send operational directives to the agricultural work machine based on the actionable information, the operational directives including instructions for altering a direction of travel of the work machine to match an optimal direction of travel generated by the model to optimize field efficiency for an identified geospatial data event, based at least in part on the physical parameters of the farm implement and the total farmable field area.
 21. The method of claim 20, wherein the operational directives for the working machine include: (i) instructions for increasing or decreasing the speed of the working machine to match an optimal speed generated by the model for an identified geospatial data event; (ii) instructions for shifting gears and reducing engine speed to match optimal levels generated by the model for an identified geospatial data event; and/or (iii) instructions to turn off the working machine during non-productive times.
 22. The method of claim 21, comprising generating and sending operational directives to the agricultural work machine in real-time or near real-time.
 23. The method of claim 20, wherein said automatically classify (e) comprises mapping the agricultural geospatial data to the geospatially located geometries of the farm to determine whether the agricultural geospatial data references a location (i) within any of the field boundaries, (ii) outside any of the field boundaries, or (iii) at an intersection of any of the field boundaries.
 24. The method of claim 23, wherein said automatically classify (e) further comprises determining whether the agricultural geospatial data references speed of the equipment within an operating speed range, or within a travel speed range.
 25. The method of claim 24, wherein said automatically classify (e) further comprises determining a type of work machine and/or implement used to generate the agricultural geospatial data.
 26. The method of claim 25, further comprising capturing, with an implement sensor disposed on the work machine, the type of work machine and/or implement.
 27. The method of claim 20, wherein the agricultural geospatial data further comprises temporal data; machine and equipment data including speed of the machine, engine load, fuel usage; agronomic data; monitor/controller data; equipment sensor data; and combinations thereof.
 28. The method of claim 27, comprising using the location data to provide a travel path of the work machine.
 29. The method of claim 20, wherein: the operational events comprise the work machine performing agricultural tasks within field boundaries; the travel events comprise the work machine moving from one field to another field; the work machine moving from one significant location to another significant location; and combinations thereof; and the ancillary events comprise the work machine engaging in operations supportive of the operational events and/or the travel events.
 30. The method of claim 29, wherein the operational events are selected from the group consisting of: planting; applications such as spraying or spreading; harvesting; tilling; grain or bulk crop carting; grain or bulk crop hauling; baling; seed tending; and preparation work such as swathing, windrowing, conditioning, raking, or tedding.
 31. The method of claim 30, wherein the ancillary events include idling and switching implements.
 32. The method of claim 20, wherein the actionable information is selected from the group consisting of: percentage of time the working machine spent idling during various ones of said geospatial data events; costs per acre associated with various ones of said geospatial data events; correlation of speed of working machine to costs associated with various ones of said geospatial data events; cost savings per acre as a function of speed of the working machine; and combinations thereof.
 33. The method of claim 20, wherein the operational data for the work machine from the ECU includes speed, fuel use and engine rpm/load data.
 34. The method of claim 20, wherein said capture (a) comprises using the GPS receiver to capture GPS coordinates of the boundaries and topographical features of a field as the agricultural work machine traverses the field.
 35. The method of claim 20, wherein said capture (a) comprises capturing coordinates of the boundaries and topographical features of a field from a map.
 36. The method of claim 20, wherein said capture (d) further comprises capturing a speed at which the farm implement is traversing the farm.
 37. The method of claim 20, wherein said aggregate (f) further comprises placing the plurality of geospatial data events into management categories.
 38. The method of claim 37, further comprising selecting the management categories from the group consisting of: business farm entities or clients, land ownership entities or farms, fields, storage locations, staging areas, equipment, operations, laborers, and combinations thereof. 